Device for hyperspectral holographic microscopy by sensor fusion

ABSTRACT

The invention concerns a device for the holographic and hyperspectral measurement and analysis (2) of a sample (3), comprising; —an acquisition means (2) for acquiring a diffracted image (11) of an image of the sample (3); and interference patterns (12) of a reference light signal (R) and the light signal (O) having passed through the sample (3) to be measured and analysed; and—a means for illuminating the sample (3) focused on the sample (3); and—a means for reconstructing and analysing (1) the hyperspectral holographic image comprising a deep convolutional neural network generating an image for analysis and detection of particularities in the sample.

TECHNICAL FIELD

The present invention relates to a microscopy device for analyzing asample.

PRIOR ART

Different methods of compressing the image of a hyperspectral scene aredescribed in the literature.

The object of these methods is to acquire the image of the hyperspectralscene in a single acquisition without the need to scan the scene inspatial or spectral dimensions.

For example, the thesis “Non-scanning imaging spectrometry”, Descour,Michael Robert, 1994, The university of Arizona, proposes a way ofacquiring a single two-dimensional image of the observed scenecontaining all the information on the influence of differentwavelengths.

This method, called CTIS (for “Computed-Tomography ImagingSpectrometer”), proposes to capture a compressed image of the observedscene by means of a diffraction grating arranged upstream of a digitalsensor. This compressed image acquired by the digital sensor takes theform of multiple projections for which each projection makes it possibleto represent the image of the observed scene and contains all thespectral information of the scene.

This method, although satisfactory for solving the problem ofinstantaneous acquisition of the hyperspectral scene, requires complexalgorithms which are costly in computing resources in order to estimatethe uncompressed hyperspectral scene in three dimensions. Thepublication “Review of snapshot spectral imaging technologies”, NathanHagen, Michael W. Kudenov, Optical Engineering 52 (9), September 2013,presents a comparison of hyperspectral acquisition methods as well asthe algorithmic complexities associated with each of them.

Indeed, the CTIS method requires an estimation process based on atwo-dimensional matrix representing the transfer function of thediffraction optics. This matrix must be inverted to reconstruct thethree-dimensional hyperspectral image. As the transfer function matrixis not completely defined, iterative matrix inversion methods which arecostly in computing resources make it possible to approach the resultstep by step.

In addition, the three-dimensional hyperspectral image reconstructed bythese calculation methods does not contain additional spatial orspectral information compared to the compressed two-dimensional imageobtained by these acquisition methods. The estimation by the calculationof the hyperspectral image in three dimensions is therefore notnecessary for a direct detection of the particularities sought in thescene.

Holography was invented in 1947 by Dennis Gabor. Since then, variousimprovements have been made to the process. In particular, digitalholography, the foundations of which were laid in 1967 by Goodman,brings the possibility of processing and reconstructing holograms from adigital acquisition of interference figures. Yamaguchi introduceddigital color holography in 2002, allowing acquisition andreconstruction of the hologram taking into account the colors of theobject. Various improvements have been proposed since. For example, thethesis «Méthodes d'holographie numérique couleur pour la métrologie sanscontact en acoustique et mécanique» (“Digital color holography methodsfor non-contact metrology in acoustics and mechanics”), Patrice Tankam,2011, presents various state-of-the-art processes for digitallyacquiring a color hologram and reconstituting it.

These methods do not offer the possibility of acquiring andreconstructing a hyperspectral hologram of the observed object.Likewise, the methods of reconstructing the hologram from theacquisition of interference figures of object and reference rays, basedon the Fresnel transform, require calculations expensive incomputational and memory resources.

Methods of image processing for the purpose of detecting features arewidely described in the scientific literature. For example a methodbased on neural networks is described in “auto-association by multilayerperceptrons and singular value decomposition». Biological cybernetics,59 (4): 291-294, 1988. ISSN 0340-1200, H. Bourlard and Y. Kamp. AT.

New methods based on deep convolutional neural networks are also widelyused with results showing very low false detection rates. For example,such a method is described in “Stacked Autoencoders Using Low-PowerAccelerated Architectures for Object Recognition in Autonomous Systems”,Neural Processing Letters, vol. 43, no. 2, pp. 445-458,2016, J. Maria,J. Amaro, G. Falcao, L. A. Alexandre.

These methods are particularly suitable for detecting elements in acolor image; generally having 3 channels—Red, Green and Blue; of a sceneby taking into account the characteristics of shapes, textures andcolors of the feature to be detected. These methods consider the imagehomogeneous, and process by convolution the entire image by the sameprocess.

The processing of the two-dimensional compressed images obtained by theCTIS method cannot therefore be operated by means of a standard deep andconvolutional neural network. Indeed, the images obtained by thesemethods are not homogeneous, and contain nonlinear features in eitherspectral or spatial dimensions.

DISCLOSURE OF THE INVENTION

The present invention proposes to directly detect the particularitiessought in a sample by means of a formal, deep and convolutional neuralnetwork, the architecture of which is adapted to a fusion of informationand direct detection, applied to a hologram of the a sample containingthe phase and amplitude information of the sample and a compressed andtwo-dimensional image of the light passing through the sample containingthe spectral information of the image of said sample.

According to one embodiment, the hologram and compressed images of thesample are thus merged by means of said formal, deep and convolutionalneural network and a direct detection of said desired features is madefrom the information merged by means of this same formal neural network,deep and convolutional.

To this end, the invention relates to a device for holographic andhyperspectral measuring and analyzing a sample, said device comprising:

-   -   an acquisition device for acquiring an image of the sample        containing spectral and amplitude information of the light        signal illuminating said sample and holographic interference        figures of a reference light beam and of a light beam having        illuminated said sample containing the amplitude and phase        information of the light signal illuminating said sample; and    -   an illumination device of said sample; and    -   a device for reconstructing the hyperspectral holographic image        and for analyzing the amplitude, phase and spectrum properties        of the light illuminating said sample, integrating a deep and        convolutional neural network architectured to calculate a        probability of presence of the one or more particularities        sought in said sample from the hyperspectral holographic image        and generating an image for each sought particularity, the value        of each pixel at the coordinates x and y corresponding to the        probability of presence of said particularity at the same x and        y coordinates of the image of said sample.

According to one embodiment, the acquisition device comprises on the onehand a device for acquiring a compressed image of the sample containingsaid spectral and amplitude information of the illuminating lightsignal, in particular passing through or reflected by the sample, and onthe other hand a device for acquiring an image of said holographicinterference figures, in which the neural network is designed tocalculate the probability of the presence of the particularities soughtin said sample from the compressed image and the figure of holographicinterference of the reference beam with the illuminating beam, inparticular passing through or reflected by the sample, said deep andconvolutional neural network being architectured so as to merge theinformation from the sensors of the diffracted image and of the image ofthe holographic interference figure.

According to this embodiment, the invention is based on a holographicsensor measuring the light having passed through the sample to beanalyzed and a sensor using a method of diffraction of the light havingpassed through the sample, and an information processing configured inorder to merge the holographic and compressed images and to detect theparticularities sought in the sample.

An embodiment using reflection is also envisaged.

According to one embodiment, the present invention uses the hologram andthe compressed image of the same hyperspectral scene. A deepconvolutional neural network image fusion method is presented in“Multimodal deep leaming for robust rgb-d object recognition. InIntelligent Robots and Systems (IROS)”, Eitel, A., Springenberg, J. T.,Spinello, L., Riedmiller, M., and Burgard, W. (2015) IEEE/RSJInternational Conférence on, pages 681 #687. IEEE. This documentpresents a deep and convolutional neural network structure using twoprocessing paths, one path per image type of the same scene, completedby layers merging the two paths; the function implemented by this deepand convolutional neural network is a classification of images. Thisstructure is not suitable as it is for this embodiment of the presentinvention, since it is not suitable for two-dimensional compressedimages of a three-dimensional hyperspectral scene, and having thefunction of classifying the scene and not detection of particularitiesin this scene.

This embodiment of the invention makes it possible to measure andanalyze a sample from a holographic image acquired from light passingthrough the sample; image containing all spatial and phase information;and a compressed image, a compressed, non-homogeneous and non-lineartwo-dimensional representation containing all the spatial and spectralinformation of the light having passed through said sample by means of aconvolutional neural network merging this information. An embodimentusing reflection is also envisaged.

The invention finds a particularly advantageous application for systemsintended to analyze samples by detecting features from their shape,texture, phase and transmittance spectrum.

The invention can be applied to a large number of technical fields inwhich hyperspectral holographic microscopic analysis is sought. In anon-exhaustive manner, the invention can be used, for example, in themedical and dental field, to aid in the diagnosis by detecting inparticular the presence of bacteria, cells or molecules in a sample. Inthe field of chemical analysis, the invention can also be used tomeasure concentrations. In the field of biology, the invention can beused to detect the presence of bacteria, cells, spores, or organicmolecules.

For the purposes of the invention, a hyperspectral holographicmicroscopy corresponds to a device detecting particularities in theimage of a sample directly from an optical system acquiring amicroscopic hologram of the sample, containing the phase and amplitudeinformation of the image of the sample and an acquisition of acompressed image containing the spatial and spectral information of thesample.

According to one embodiment, the illumination device for illuminatingsaid sample comprises a collimated light source configured to generate alight beam, the acquisition device for acquiring said diffracted imageand said image of holographic interference figures comprises:

-   -   a first semi-reflecting mirror separating the light beam from        said light source into two light beams; a first beam, object        beam, passing through the sample and a second beam, reference        beam, towards a second reflecting mirror; and    -   the second reflecting mirror directing said reference light beam        towards a third semi-reflecting mirror; and    -   the third semi-reflecting mirror, adding said reference beam        with said object beam and transmitted in the direction of a        chromatic filter; and    -   an area in which said sample can be positioned so as to be        traversed by said object light beam; and    -   a fourth semi-reflecting mirror, separating said object beam        coming from the area in which said sample can be positioned into        two beams: a third beam being transmitted towards the third        semi-reflecting mirror and a fourth beam being transmitted        towards a first converging lens; and    -   the first converging lens configured to image said sample        (illuminated by said object beam) at an opening; and    -   a collimator configured to pick up the beam passing through said        opening and to transmit this beam on a diffraction grating; and    -   a second converging lens configured to focus the rays coming        from the diffraction grating on a capture surface,    -   the chromatic filter configured to filter the wavelengths of        said object and reference beams, added and interfered into a        hologram on the third semi-reflective mirror; and    -   a third converging lens configured to focus the hologram rays        coming from the chromatic filter on a capture surface.

This embodiment is particularly simple to achieve.

According to one embodiment, the acquisition device comprises a singledevice for acquiring a compressed image of the holographic interferencefigures of the sample.

According to this embodiment, the illumination device for illuminatingsaid sample comprises a collimated light source configured to generate alight beam, and the acquisition device comprises:

-   -   a first semi-reflecting mirror separating the light beam from        said light source into two light beams: a first beam, object        beam, illuminating the sample and a second beam, reference beam;        and    -   an area in which said sample can be positioned so as to be        imaged by said object light beam; and    -   a system of mirrors adapted to have the object and reference        beams interfere,    -   a first converging lens configured to image said hologram of the        sample on an opening; and    -   a collimator configured to pick up the beam passing through said        opening and to transmit this beam on a diffraction grating; and    -   a second converging lens configured to focus the rays coming        from the diffraction grating on a capture surface.

According to one embodiment, said illumination device is obtained by alight source comprising:

-   -   a first source of white, multi-chromatic and non-coherent light;        and    -   a first converging lens configured to collimate light rays from        said first source of white, multi-chromatic and non-coherent        light; and    -   a second source of mono-chromatic and coherent light; and    -   a beam expanding optical system configured to extend and        collimate light rays from said second mono-chromatic and        coherent light source; and    -   a third converging lens configured to collimate light rays from        said second diverging lens; and    -   a prism configured to add the light rays coming from said        mono-chromatic and coherent light source and the light rays        coming from said white, multi-chromatic and non-coherent light        source in a light beam.

This embodiment is particularly simple to achieve.

According to one embodiment, said holographic interference figure isobtained by an infrared sensor. This embodiment makes it possible toobtain information that is invisible to the human eye.

According to one embodiment, said holographic interference figure isobtained by a sensor whose wavelength is between 300 nanometers and 2000nanometers. This embodiment makes it possible to obtain information inthe domains visible and invisible to the human eye.

According to one embodiment, said compressed image is obtained by aninfrared sensor. This embodiment makes it possible to obtain informationthat is invisible to the human eye.

According to one embodiment, said compressed image is obtained by asensor whose wavelength is between 300 nanometers and 2000 nanometers.This embodiment makes it possible to obtain information in the domainsvisible and invisible to the human eye.

According to one embodiment, said particularity sought in said sample isthe presence of a kind and a species of bacteria in a sample of saliva,dental tartar sample, nasal secretions, blood or urine containing a setof bacteria of different kinds and different species. This embodimentmakes it possible to detect certain bacteria indicative of diseases orsyndromes for the purpose of aid in diagnosis.

According to one embodiment, said particularity sought in said sample isthe presence of a molecule or of a set of molecules exhibiting aparticular transmittance in the light spectrum concerned by theanalysis. This embodiment makes it possible to detect certain moleculesindicative of diseases or syndromes for the purpose of assisting indiagnosis.

According to one embodiment, said particularity sought in said sample isthe presence of gametes in a sperm sample. This embodiment makes itpossible to count the gametes present in a sample for the purpose of aidin diagnosis.

According to another aspect, the invention relates to a method formeasuring and analyzing a holographic and hyperspectral sample, saidmethod comprising:

-   -   an illumination device illuminates said sample; and    -   an acquisition device acquires an image containing the spectral        and amplitude information of the light signal illuminating said        sample; and holographic interference figures of a reference        light beam and a light beam having illuminated said sample        containing the amplitude and phase information of the light        signal illuminating said sample; and    -   a device for reconstructing the hyperspectral holographic image        and analyzing the amplitude, phase and spectrum properties of        the light illuminating said sample integrates a deep and        convolutional neural network architectured to calculate a        probability of the presence of the one or more particularities        sought in said sample from the hyperspectral holographic image,        and generate an image for each sought particularity whose value        of each pixel at the coordinates x and y corresponds to the        probability of the presence of said particularity at the same        coordinates x and y of said sample

In another aspect, the invention relates to a computer programcomprising instructions which cause a processor to perform such amethod.

As regards the detection of particularities from said holographic andcompressed images, the invention uses a deep and convolutional neuralnetwork for determining the probabilities of the presence of thesefeatures. Learning of said deep and convolutional neural network makesit possible to indicate the probability of presence of theparticularities sought for each x and y coordinates of the image of saidsample. For example, learning by backpropagation of the gradient or itsderivatives from training data can be used.

The deep convolutional neural network for direct detection from saidholographic image and said compressed image has an input layer structuresuitable for direct detection. The invention has several architecturesof the deep layers of said neural network. Among this, an auto-encoderarchitecture as described in the document “SegNet: A Deep ConvolutionalEncoder-Decoder Architecture for Image Segmentation”, VijayBadrinarayanan, Alex Kendall, Roberto Cipolla makes it possible toindicate the probability of the presence of the particularities soughtfor each x and y coordinates of the image of said sample. In addition,the document “Multimodal deep learning for robust rgb-d objectrecognition. In Intelligent Robots and Systems (IROS)”, Eitel, A.,Springenberg, J. T., Spinello, L., Riedmiller, M., and Burgard, W.(2015) IEEE/RSJ International Conference on, pages 681 #687. IEEE,describes a convolutional neural network architecture suitable forprocessing different images of the same scene.

Said input layers of the neural network are adapted so as to be filledwith the data of said holographic image and said compressed imageobtained by the acquisition device. Thus, each input layer of thedifferent image processing paths is a tensor of order three and has twospatial dimensions of size XMAX and YMAX, and a depth dimension of sizeDMAX.

The invention uses the nonlinear relation f (x_(t), y_(t),d_(t))->(x_(img), y_(img)) defined for x_(t)ϵ[0 . . . XMAX[, y_(t)ϵ[0YMAX[and d_(t)ϵ[0 . . . DMAX[making it possible to calculate the x_(img)and y_(img) coordinates of the pixel of said at least one compressedimage whose intensity is copied into the third-order tensor of saidinput layer of the compressed images processing path of the neuralnetwork at coordinates (x_(t), y_(t), d_(t)).

The acquisition device acquires said compressed image containingdiffractions of the image of said sample obtained with diffractionfilters. Said diffracted image obtained contains the image of the sceneof the non-diffracted sample at the center, as well as the projectionsdiffracted along the axes of the various diffraction filters. An inputlayer of the neural network contains a copy of the eight chromaticrepresentations of the hyperspectral scene of the sample of thediffracted image according to the following non-linear relationship:

$\begin{matrix}{{f\left( {x_{t},y_{t},d_{t}} \right)} = \begin{Bmatrix}{x_{img} = {x + {x_{offsetX}(n)} + {\lambda \cdot \lambda_{sliceX}}}} \\{y_{img} = {y + {y_{offsetY}(n)} + {\lambda \cdot \lambda_{sliceY}}}}\end{Bmatrix}} & \;\end{matrix}$

with:

f (x_(t), y_(t), d_(t)) function calculating the value of the inputlayer at position x_(t), y_(t), d_(t);n=floor (d_(t)/dMAX);λ=d_(t) mod (dMAX/7);n between 0 and 7, the number of diffractions of the compressed image;d_(t) included between 0 and DMAX;x_(t) included between 0 and XMAX;y_(t) between 0 and YMAX;DMAX, the depth constant of the third order tensor of said input layer;λ_(slicex), the spectral pitch constant of the pixel in X of saidcompressed image;λ_(sliceY), the spectral pitch constant of the pixel in Y of saidcompressed image;x_(offsetx)(n) corresponding to the offset along the X axis ofdiffraction n;y_(offsety)(n) corresponding to the offset along the Y axis ofdiffraction n.

The architecture of the deep and convolutional neural network iscomposed of an encoder for said holographic image and an encoder forsaid compressed image; each of the encoders making it possible to searchfor the elementary characteristics specific to the desired detection;followed by a decoder making it possible to merge the specificelementary characteristics of each of the images processed by theencoders and to generate an image of the probabilities of the presenceof the characteristics to be detected in the sample. The encoder/decoderstructure makes it possible to search for the basic characteristicsspecific to the main characteristic sought in the image of said sample.

Each encoder is composed of a succession of layers of convolutionalneurons alternating with pooling layers (decimation operator of theprevious layer) making it possible to reduce the spatial dimension.

The decoder is composed of a succession of layers of deconvolutionneurons alternating with unpooling layers (interpolation operation ofthe previous layer) allowing an increase in the spatial dimension.

For example, such an encoder/decoder structure is described in “SegNet:A Deep Convolutional Encoder-Decoder Architecture for ImageSegmentation”, Vijay Badrinarayanan, Alex Kendall, Roberto Cipolla.

For example, such a structure of encoders merging different images ofthe same scene is described in “Multimodal deep learning for robustrgb-d object recognition. In Intelligent Robots and Systems (IROS)”,Eitel, A., Springenberg, J. T., Spinello, L., Riedmiller, M., andBurgard, W. (2015) IEEE/RSJ International Conference on, pages 681 #687.IEEE.

A set of fully connected neural layers can be positioned between theencoder and the decoder.

BRIEF DESCRIPTION OF THE FIGURES

The manner of carrying out the invention as well as the advantages whichresult therefrom will emerge from the following embodiment, given as anindication but not limited to, in support of the appended figures inwhich FIGS. 1 to 11 represent:

FIG. 1: a schematic front view of the elements of a hyperspectralholographic microscopy device according to one embodiment of theinvention;

FIG. 2: a structural schematic representation of the elements of thedevice of FIG. 1;

FIG. 3: a schematic representation of the optics of the device in FIG.2;

FIG. 4: a schematic representation of the architecture of the neuralnetwork in FIG. 2.

FIG. 5: a schematic representation of the connection of the first layersof the neural network of FIG. 2;

FIG. 6: a schematic representation of the light source of FIG. 2.

FIGS. 7 to 11 are schematic representations similar to FIG. 3 for otherembodiments of optics.

WAY OF DESCRIBING THE INVENTION

FIG. 1 illustrates a capture device 2 for capturing the light passingthrough a sample 3, said capture device 2 comprising measurement optics,a holographic sensor making it possible to obtain a hologram of thesample 3 and a sensor making it possible to obtain a compressed image oflight passing through the sample 3. In this embodiment, the term“hyperspectral holographic image” therefore denotes an image comprisingthe spatial juxtaposition of a compressed image comprising thehyperspectral information and an image of a hologram. The capture device2 transmits the images obtained to a processing device 1 merging theinformation contained in the hologram of the sample 3 and the compressedimage of light passing through the sample 3 in order to detectparticularities in the sample 3.

As shown in FIG. 2, the capture device 2 comprises a device foracquiring a compressed image of the light passing through the sample 3,which comprises a first converging lens 21 which focuses the image ofthe sample 3 on an opening 22. A collimator 23 captures the rays passingthrough the opening 22 and transmits these rays to a diffraction grating24. A second converging lens 25 focuses these rays coming from thediffraction grating 24 onto a capture surface 26.

The structure of this optical assembly is relatively similar to thatdescribed in the scientific publication “Computed-tomography imagingspectrometer: experimental calibration and reconstruction results”,published in APPLIED OPTICS, volume 34 (1995) number 22.

This optical structure makes it possible to obtain a compressed image11, illustrated in FIG. 5, showing several diffractions R0-R7 of theimage of the sample 3 arranged around an undiffracted image of smallsize C. In the example of FIG. 5, the compressed image shows eightdistinct diffractions R0-R7 obtained with two diffraction axes of thediffraction grating 24.

As a variant, three diffraction axes can be used on the diffractiongrating 24 so as to obtain a diffracted image 11 with sixteendiffractions.

As illustrated in FIG. 2, the capture device 2 comprises a device foracquiring a holographic image of the sample 3, which comprises aconverging lens 31 which focuses the hologram of the sample 3 on acapture surface 32.

This structure makes it possible to obtain a holographic image 12,illustrated in FIG. 5, containing the hologram of the sample 3.

The processing device 1 comprises a neural network 13 merging theinformation contained in the images 11 and 12 and generates an image 14of which each pixel at coordinates x and y indicates the probability ofpresence of the particularity sought in the sample 3 at the same x and ycoordinates of the sample 3 plane.

Alternatively, the processing device 1 comprises a neural network 13configured to merge the information contained in the images 11 and 12and generates an image 14 representing the sample as it would be seen bya standard microscope.

Thus, according to an independent aspect, an invention relates to adevice for measuring a sample, said device comprising:

-   -   a capture device 2 for acquiring a compressed image 11 of the        sample 3 containing spectral and amplitude information of the        light signal illuminating said sample 3 and holographic        interference FIG. 12 of a reference light beam R and of a light        beam O having illuminated said sample 3 containing the amplitude        and phase information of the light signal illuminating said        sample; and    -   an illumination device 34 of said sample 3; and    -   a device for reconstructing a microscopy image of the sample        integrating a deep and convolutional neural network 13        architectured to calculate a light intensity in said sample 3        from the compressed image 11 and the holographic interference        FIG. 12 of the beam of reference R with the beam O illuminating        the sample 3, and generating an image whose value of each pixel        at the coordinates u and v corresponds to the light intensity at        the x and y coordinates of the plane of said sample 3; said deep        and convolutional neural network 13 being architectured so as to        merge the information of the sensors of the diffracted image 11        and of the image of the holographic interference FIG. 12.

The neural network is configured to reconstruct the microscopic imagefrom the detections made.

The image (u; v) is magnified relative to the area (x; y) of the sampleplane imaged.

As this aspect in itself appears to be innovative, the applicantreserves the right to protect it in itself, independently, by anyappropriate means from the present patent application.

The optical device 41 comprises, as illustrated in FIG. 3, the followingelements:

-   -   a light source, as illustrated in FIG. 6, configured so as to        generate a light beam comprising a white, multichromatic and        non-coherent light 64 and a mono-chromatic and coherent light        beam 61 transmitted through said sample 3; and    -   an optical path configured so as to generate a reference light        beam R comprising the semi-reflecting mirror 35 and the        reflecting mirror 36; and    -   an area allowing the beam from the first semi-reflecting mirror        35 to pass through said sample 3 so as to generate an object        beam O containing the light that has passed through sample 3;        and    -   a semi-reflecting mirror 38 configured so as to transmit said        object light beam O in the direction of the capture device for        acquiring the diffracted image, the first element of which is        the converging lens 21 and toward the semi-reflecting mirror 37        configured in such a way as to have said object light beam O and        reference light beam R interfere; the interference figures        generated on the semi-reflecting mirror 37 are the hologram of        said sample 3;    -   a semi-reflecting mirror 37 generating the hologram of said        sample 3 and transmitting said hologram towards the holographic        acquisition device, the first element of which is the chromatic        filter 33, retaining the wavelengths of the monochromatic light        beam.

The light beam comprising white, multi-chromatic and non-coherent lightis emitted by a white, multi-chromatic and non-coherent light source 64and the mono-chromatic and coherent light beam is emitted by a monochromatic and coherent light beam source 61.

The optical housing 40 is obtained by placing the sample 3 in thededicated area of the optical device 41.

The capture surfaces 26, and 32 may correspond to a CCD sensor (for“charge-coupled device”), to a CMOS sensor (for “Complementarymetal-oxide-semiconductor”, a technology for manufacturing electroniccomponents), or to any other known sensor. For example, the scientificpublication “Practical Spectral Photography”, published in Eurographics,volume 31 (2012) number 2, proposes to associate the diffraction opticalstructure with a standard digital camera to capture the compressedimage.

Preferably, each pixel of the compressed 11 and holographic 12 images iscoded on three colors red, green and blue and on 8 bits thus making itpossible to represent 256 levels on each color.

As a variant, the sensing surfaces 26, or 32 can be a device the sensedwavelengths of which are not in the visible field. For example, thedevice 2 can integrate sensors whose wavelength is between 300nanometers and 2000 nanometers.

When the compressed 11, and holographic 12 images of the observed sample3 are obtained, the detection means implements a neural network 13 todetect a feature in the observed scene from the information of thecompressed 11, and holographic 12 images.

This neural network 13 aims at determining the probability of presenceof the desired particularity for each pixel located at the x and ycoordinates of the observed hyperspectral scene 3.

To do this, as illustrated in FIG. 4, the neural network 13 includes anencoder 51 for the compressed image and an encoder 51 for theholographic image; each encoder has an input layer 50, able to extractthe information from the image 11, or 12. The neural network merges theinformation from the two encoders 51 by means of convolutional layers orfully connected layers. A decoder 53 and its output layer, able toprocess this information so as to generate an image 14 whose intensityof each pixel at the x and y coordinate corresponds to the probabilityof presence of the particularity at the x and y coordinates of thesample 3 is inserted following the merging of the information.

As illustrated in FIG. 5, said input layers 50 of the encoders 51 of theneural network 13 are filled with information from said compressed image11 and said holographic image 12.

The input layer 50 of the encoder 51 processing the information of saidholographic image 12 is filled with a copy of said holographic image 12,each pixel of which is scaled by means of a multiplication by a constantallowing each pixel to be in the range [0 . . . 1].

The input layer 50 of the encoder 51 processing the information of saidcompressed image 11 is filled according to the following non-linearrelationship:

${f\left( {x_{t},y_{t},d_{t}} \right)} = \begin{Bmatrix}{x_{img} = {x + {x_{offsetX}(n)} + {\lambda \cdot \lambda_{sliceX}}}} \\{y_{img} = {y + {y_{offsetY}(n)} + {\lambda \cdot \lambda_{sliceY}}}}\end{Bmatrix}$

with

f (x_(t), y_(t), d_(t)) function calculating the value of the inputlayer at position x_(t), y_(t), d_(t);

n=floor (d_(t)/dMAX);

λ=d_(t) mod(dMAX/7);

n between 0 and 7, the number of diffractions of the compressed image;

d_(t) included between 0 and DMAX;

x_(t) included between 0 and XMAX;

y_(t) between 0 and YMAX;

DMAX, the depth constant of the third order tensor of said input layer;

λ_(slicex), the spectral pitch constant of the pixel in X of saidcompressed image;

λ_(sliceY), the spectral pitch constant of the pixel in Y of saidcompressed image;

x_(offsetx)(n) corresponding to the offset along the X axis of thediffraction n;

y_(offsetx)(n) corresponding to the offset along the Y axis ofdiffraction n.

Floor is a well-known truncation operator.

Mod stands for the “modulo” operator.

The architecture of said neural network 13 is composed of a set ofconvolutional layers such as layer 50 assembled linearly and alternatelywith decimation (pooling) or interpolation (unpooling) layers.

A convolutional layer of depth d, denoted CONV (d), is defined by dconvolution kernels, each of these convolution kernels being applied tothe volume of the input tensor of order three and of size X_(input),Y_(input), d_(input). The convolutional layer thus generates an outputvolume, tensor of order three, having a depth d. An activation functionACT is applied to the calculated values of the output volume.

The parameters of each convolutional kernel of a convolutional layer arespecified by the neural network training procedure.

Different ACT activation functions can be used.

For example, this function can be a ReLu function, defined by thefollowing equation:

ReLu(x)=max(0,x)

A decimation layer makes it possible to reduce the width and height ofthe third order input tensor for each depth of said third order tensor.For example, a MaxPool (2,2) decimation layer selects the maximum valueof a sliding tile on the surface of 2×2 values. This operation isapplied to all the depths of the input tensor and generates an outputtensor having the same depth and a width divided by two, as well as aheight divided by two.

A neural network architecture allowing the direct detection of featuresin the hyperspectral scene can be as follows:

Input l Input 2 ⇒CONV(64) ⇒CONV(64) ⇒MaxPool(2,2) ⇒MaxPool(2,2)⇒CONV(64) ⇒CONV(64) ⇒MaxPool(2,2) ⇒MaxPool(2,2) ⇒CONV(64) ⇒CONV(64)⇒MaxUnpool(2,2) ⇒CONV(64) ⇒MaxUnpool(2,2) ⇒CONV(64) ⇒MaxUnpool(2,2)⇒CONV(1) ⇒Output

Alternatively, the number of CONV(d) convolution and MaxPool (2.2)decimation layers can be changed in order to facilitate the detection ofparticularities having higher semantic complexity. For example, a highernumber of convolutional layers makes it possible to process more complexsignatures of shape, texture, or spectral characteristics of theparticularity sought in the hyperspectral scene.

Alternatively, the number of CONV(d) deconvolution and MaxUnpool (2, 2)interpolation layers can be changed to facilitate reconstruction of theoutput layer. For example, a higher number of deconvolution layers makesit possible to reconstruct an output with greater precision.

Alternatively, the convolution layers CONV(64), may have a differentdepth than 64 in order to handle a different number of localparticularities. For example, a depth of 128 makes it possible tolocally process 128 different particularities in a complex hyperspectralscene.

Alternatively, the interpolation layers MaxUnpool(2, 2) can be ofdifferent interpolation dimension. For example, a layer MaxUnpool(4, 4)can increase the processing dimension of the top layer.

As a variant, the activation layers ACT of ReLu (x) type insertedfollowing each convolution and deconvolution can be of a different type.For example, the softplus function defined by the equation: f (x)=log(1+e^(x)) can be used.

Alternatively, the decimation layers MaxPool(2, 2) can be of differentdecimation size. For example, a layer MaxPool(4, 4) makes it possible toreduce the spatial dimension more quickly and to concentrate thesemantic research of the neural network on the local particularities.

Alternatively, fully connected layers can be inserted between the twocentral convolution layers at line 6 of the description in order toprocess detection in a higher mathematical space. For example, threefully connected layers of size 128 can be inserted.

Alternatively, the dimensions of the convolution layers CONV(64),decimation layers MaxPool(2, 2), and interpolation layers MaxUnpool(2,2) can be adjusted on one or more layers, in order to adapt thearchitecture of the neural network closest to the type ofparticularities sought in the hyperspectral scene.

The weights of said neural network 13 are calculated by means oftraining. For example, learning by backpropagation of the gradient orits derivatives from training data can be used to calculate theseweights.

Alternatively, the neural network 13 can determine the probability ofthe presence of several distinct particularities within the sameobserved scene. In this case, the last convolutional layer will have adepth corresponding to the number of distinct features to be detected.Thus the convolutional layer CONV(1) is replaced by a convolutionallayer CONV(u), where u corresponds to the number of distinctparticularities to be detected.

As a variant, normalization layers, for example of the BatchNorm orGroupNorm type, as described in “Batch Normalization: Accelerating DeepNetwork Training by Reducing Internal Covariate Shift”, Sergey Ioffe,Christian Szegedy, February 2015 and “Group Normalization”, Yuxin Wu,Kaiming He, FAIR, June 2018, can be inserted before or after eachactivation layer or at different levels of the neural network structure.

The weights of said neural network 13 are calculated by means oftraining. For example, learning by backpropagation of the gradient orits derivatives from training data can be used to calculate theseweights.

Alternatively, the neural network 13 can determine the probability ofthe presence of several distinct particularities within the sameobserved scene. In this case, the last convolutional layer will have adepth corresponding to the number of distinct features to be detected.Thus the convolutional layer CONV (1) is replaced by a convolutionallayer CONV (u), where u corresponds to the number of distinct featuresto be detected.

As illustrated in FIG. 6, the illumination means 34 of sample 3 isobtained by a device comprising:

-   -   a first source of white, multi-chromatic and non-coherent light        64; and    -   a first converging lens 65 configured to collimate light rays        from said source of white, multi-chromatic and non-coherent        light 64; and    -   a second source of mono-chromatic and coherent light 61; and    -   an optical system 62 for expanding the beam configured to extend        and collimate the light rays coming from said source of        monochromatic and coherent light 61 (this optical system for        expansion comprises, for example, along the optical path, a        second divergent lens then a third converging lens collimating        the beam from the second diverging lens); and    -   a prism 67 configured to add the light rays 63 coming from said        mono-chromatic and coherent light source 61 and the light rays        66 coming from said white, multi-chromatic and non-coherent        light source 64 in a light ray 68 directed towards the sample 3.

FIG. 7 shows schematically the optical assembly of another embodiment ofthe invention. One of the peculiarities of the embodiment of FIG. 7 isthat the fusion between the diffraction and the hologram takes placeoptically rather than through the neural network. The optical assemblyshown in FIG. 7 therefore makes it possible to obtain a compressed imageof the hologram. Therefore, in this embodiment, by the term“hyperspectral holographic image” is meant a compressed image (includingdiffraction) of a holographic image of the sample.

More specifically, the optical device shown in FIG. 7 comprises thefollowing elements:

-   -   a light source 34;    -   an optical path configured so as to generate a reference light        beam R comprising a semi-reflecting mirror 35, and the        reflecting mirror 36; and    -   a zone allowing the beam from the first semi-reflecting mirror        35 to pass through said sample 3 so as to generate an object        beam O containing the light having passed through the sample 3,        and the reflecting mirror 38; and    -   a semi-reflecting mirror 37 generating the hologram of said        sample 3 by adding the reference beam from the reflecting mirror        36 and the object beam from the reflecting mirror 38, and        transmitting said hologram towards the acquisition device; and    -   the acquisition device which comprises a first converging lens        21 which focuses the holographic image of sample 3 on an opening        22, a collimator 23 captures the rays passing through the        opening 22 and transmits these rays to a diffraction grating 24,        a second converging lens 25 which focuses these rays coming from        the diffraction grating 24 on the capture surface 26.

Thus, more precisely, the optical mixing produced on the mirror 37comprises both the interference between the coherent mono-chromaticcomponent of the object beam and of the reference beam, but also atleast the entire beam transmitted through the sample. It is this entiresignal that is submitted to diffraction. The neural network isconfigured to retrieve from the acquired image the parts of the signalallowing it to measure the desired characteristic. An intermediate stepimplemented by the neural network may be to split a part of the signalcorresponding to the hologram from the signal parts corresponding to thediffraction. However, the configuration of the neural network will notnecessarily implement such a separation.

The neural network input layer of this embodiment may be populated likethe neural network input layer of the first embodiment populated withthe compressed image.

A neural network architecture allowing the direct detection of featuresin the hyperspectral scene can be as follows:

-   -   Input        -   CONV(64)        -   MaxPool(2,2)        -   CONV(64)        -   MaxPool(2,2)        -   CONV(64)        -   CONV(64)        -   MaxUnpool(2,2)        -   CONV(64)        -   MaxUnpool(2,2)        -   CONV(64)        -   MaxUnpool(2,2)        -   CONV(1)        -   Output

The variants of neural networks discussed above are also applicable tothis embodiment.

FIG. 8 shows schematically the optical assembly of another embodiment ofthe invention. Just like in the embodiment of FIG. 7, in the embodimentof FIG. 8, the fusion of the diffraction and the hologram is doneoptically rather than by the neural network. The optical assembly shownin FIG. 8 therefore makes it possible to obtain a compressed image ofthe hologram. One of the peculiarities of the embodiment of FIG. 8 isthat the hologram is made in reflection rather than in transmission.

More specifically, the optical device shown in FIG. 8 comprises thefollowing elements:

-   -   a light source 34;    -   an optical path configured so as to generate a reference light        beam R comprising a semi-reflecting mirror 35, and the        reflecting mirrors 36 and 38; and    -   an area allowing a beam from the first semi-reflecting mirror 35        to be reflected by said sample 3 so as to generate an object        beam O containing the light reflected by sample 3, and the        semi-reflecting mirror 37; and    -   a semi-reflecting mirror 37 generating the hologram of said        sample 3 by adding the reference beam from the reflecting mirror        38 and the object beam, and transmitting said hologram towards        the acquisition device; and    -   the acquisition device which comprises a first converging lens        21 which focuses the holographic image of the sample 3 on an        opening 22, a collimator 23 captures the rays passing through        the opening 22 and transmits these rays to a diffraction grating        24, a second converging lens 25 which focuses these rays coming        from the diffraction grating 24 on the capture surface 26.

The associated neural network can have the same architecture aspresented above, the fact that the acquisition is done by reflectionrather than by transmission being reflected in the parameters of theneural network.

FIG. 9 shows schematically the optical assembly of another embodiment ofthe invention. Just like in the embodiment of FIG. 3, in the embodimentof FIG. 9, the fusion of the diffraction and the hologram is done by theneural network. One of the peculiarities of the embodiment of FIG. 9 isthat the hologram is made in reflection rather than in transmission.

More specifically, the optical device shown in FIG. 9 comprises thefollowing elements:

-   -   a light source 34;    -   an optical path configured so as to generate a reference light        beam R comprising a semi-reflecting mirror 35, and the        reflecting mirrors 36 and 38; and    -   an area allowing a beam from the first semi-reflecting mirror 35        to be reflected by said sample 3 so as to generate an object        beam O containing the light reflected by sample 3, and the        semi-reflecting mirror 37; and    -   a semi-reflecting mirror 37 generating the hologram of said        sample 3 by adding the reference beam from the reflecting mirror        38 by transmission and the object beam by reflection, and        transmitting said hologram in the direction of the holographic        image acquisition device whose first element is the chromatic        filter 33; and also transmitting the object beam O to the device        for acquiring the compressed image which comprises a first        converging lens 21 which focuses the image reflected by the        sample 3 on an opening 22, a collimator 23 which captures the        rays passing through the opening 22 and transmits these rays to        a diffraction grating 24, a second converging lens 25 which        focuses these rays coming from the diffraction grating 24 on the        capture surface 26.

In these reflective embodiments, control of the optical path between thesample 3 and the light source 34 is necessary. It is carried out bymeans of an adjustment device 69, for example of the micrometric screwtype, arranged between the sample holder and the mirror 35.

FIG. 10 schematically shows the optical assembly of another embodimentof the invention. As in the embodiment of FIG. 8, in the embodiment ofFIG. 10, the fusion of the diffraction and the hologram is doneoptically. One of the peculiarities of the embodiment of FIG. 10 is thatthe hologram is made in reflection rather than in transmission. Inaddition, the number of mirrors is reduced, which simplifies theproduction.

More specifically, the optical device shown in FIG. 10 comprises thefollowing elements:

-   -   a light source 34;    -   an optical path configured so as to generate a reference light        beam R comprising a semi-reflecting mirror 35, and a reflecting        mirror 36; and    -   a zone allowing a beam coming from the first semi-reflecting        mirror 35 to be reflected by said sample 3 so as to generate an        object beam O containing the light reflected by the sample 3,        and again the semi-reflecting mirror 35 generating the hologram        of said sample 3 by adding the reference beam from the        reflecting mirror 36 and the object beam by reflection on the        sample, and transmitting said hologram in the direction of the        acquisition device;    -   the acquisition device which comprises a first converging lens        21 which focuses the holographic image of sample 3 on an opening        22, a collimator 23 captures the rays passing through the        opening 22 and transmits these rays to a diffraction grating 24,        a second converging lens 25 which focuses these rays coming from        the diffraction grating 24 on the capture surface 26.

In this embodiment, the adjustment device 69 is for example arrangedbetween the mirror 35 and the mirror 36 in order to adjust the positionof the mirror 36.

FIG. 11 schematically shows the optical assembly of another embodimentof the invention. Just like in the embodiment of FIG. 3, in theembodiment of FIG. 11, the merging between the compression and thehologram is done by the neural network. One of the peculiarities of theembodiment of FIG. 11 is that the hologram is made in reflection ratherthan in transmission. In addition, the number of mirrors is reduced,which simplifies the production.

More specifically, the optical device shown in FIG. 11 comprises thefollowing elements:

-   -   a light source 34;    -   an optical path configured so as to generate a reference light        beam R comprising a semi-reflecting mirror 35, and a reflecting        mirror 36; and    -   an area allowing a beam from the first semi-reflective mirror 35        to be reflected by said sample 3 so as to generate an object        beam O containing the light reflected by sample 3; and    -   the semi-reflecting mirror 35 on the one hand generating the        hologram of said sample 3 by adding the reference beam from the        reflecting mirror 36 and the object beam, and transmitting said        hologram towards the semi-reflecting mirror 37,    -   the semi-reflecting mirror 37 separating this signal on the one        hand towards the holographic image acquisition device, the first        element of which is the chromatic filter 33; and on the other        hand to the device for acquiring the compressed image which        comprises a first converging lens 21 which focuses the image        reflected by the sample 3 on an opening 22, a collimator 23        which captures the rays passing through the opening 22 and        transmits these rays to a diffraction grating 24, a second        converging lens 25 which focuses these rays coming from the        diffraction grating 24 on the capture surface 26.

Some of the methods described herein may be partially implemented by aprocessor of a computer running a computer program includinginstructions for performing these methods. The computer program can berecorded on a computer readable medium.

REFERENCES

-   Capture device 2-   Sample 3-   Holographic image 12-   Compressed image 11-   Neural network 13-   Image 14-   Converging lens 21-   Opening 22-   Collimator 23-   Diffraction grating 24-   Second converging lens 25-   Capture surfaces 26, 32-   Converging lens 31-   Illumination device 34-   Semi-reflecting mirror 35, 37, 38-   Reflective mirror 36-   Optical device 41-   Layer 50-   Encoder 51-   Decoder 53-   Mono-chromatic and coherent light source 61-   Optical system 62-   Light rays 63-   Multichromatic and non-coherent white light source 64-   First converging lens 65-   Light rays 66-   Prism 67-   Light ray 68-   Adjustment device 69

1. Device for holographic and hyperspectral measuring and analyzing of asample, wherein said device comprises: an acquisition device of an imagecontaining spectral and amplitude information of the light signalilluminating said sample; and holographic interference figures of areference light bear and of a light beam having illuminated said samplecontaining the amplitude and phase information of the light signalilluminating said sample; and an illumination device of said sample; anda device for reconstructing the hyperspectral holographic image andanalyzing the amplitude, phase and spectrum properties of the lightilluminating said sample integrating a deep and convolutional neuralnetwork architectured for calculating a probability of presence of theparticularity sought in said sample from the hyperspectral holographicimage, and generating an image for each sought particularity whose valueof each pixel at the x and y coordinates corresponds to the probabilitypresence of said particularity at the same x and y coordinates of saidsample.
 2. Device according to claim 1, in which the acquisition devicecomprises a device for acquiring a compressed image of the samplecontaining said spectral and amplitude information of the illuminatinglight signal, and a device for acquiring an image of said holographicinterference figures, in which the neural network is architectured tocalculate the probability of the presence of the particularity sought insaid sample from the compressed image and the figure of holographicinterference of the reference beam with the illuminating beam, said deepconvolutional neural network being architectured so as to merge theinformation from the sensors of the diffracted image and of the image ofthe holographic interference figure.
 3. Device according to claim 2, inwhich the illumination device of said sample comprises a light sourcecollimated and configured so as to generate a light beam, in which theacquisition device for acquiring said diffracted image and said image ofthe holographic interference figures comprises: a first semi-reflectingmirror separating the light beam from said light source into two lightbeams: a first object beam, passing through the sample and a secondreference beam towards a second reflecting mirror; and the secondreflecting mirror directing said reference light beam towards a thirdsemi-reflecting mirror; and the third semi-reflecting mirror, addingsaid reference beam with said object beam and transmitted towards achromatic filter; and an area in which said sample can be positioned soas to be traversed by said object light beam; and a fourthsemi-reflective mirror, separating said object beam coming from the areain which said sample can be positioned into two beams: a third beambeing transmitted in the direction of the third semi-reflecting mirrorand a fourth beam being transmitted towards a first converging lens; andthe first converging lens configured to image said sample over anopening; and a collimator configured to pick up the beam passing throughsaid opening and to transmit this beam on a diffraction grating; and asecond converging lens configured to focus the rays coming from thediffraction grating on a capture surface, the chromatic filterconfigured to filter the wavelengths of said object and reference beams,added and interfered into a hologram on the third semi-reflectingmirror; and a third converging lens configured to focus the hologramrays coming from the chromatic filter on a capture surface.
 4. Thedevice of claim 1, wherein the acquisition device comprises a singledevice for acquiring a compressed image of the holographic interferencefigures of the sample.
 5. Device according to claim 4, in which theillumination device for illuminating said sample comprises a lightsource collimated and configured so as to generate a light beam, inwhich the acquisition device comprises a first semi-reflecting mirrorseparating the light beam from said light source into two light beams: afirst object beam, illuminating the sample (3) and a second referencebeam (R); and an area in which said sample can be positioned so as to beimaged by said object light beam; and a system of mirrors adapted tohave the object and reference beams interfere, a first converging lensconfigured to image said hologram of the sample on an opening; and acollimator configured to pick up the beam passing through said openingand to transmit this beam on a diffraction grating; and a secondconverging lens configured to focus the rays coming from the diffractiongrating on a capture surface.
 6. Device according to claim 1, whereinsaid illumination device is obtained by a light source comprising: afirst source of white, multi-chromatic and non-coherent light; and afirst converging lens configured to collimate light rays from said firstsource of white, multi-chromatic and non-coherent light; and a secondsource of monochromatic and coherent light; and a beam expanding opticalsystem configured to extend and collimate light rays from said secondmono-chromatic and coherent light source; and a prism configured to addthe light rays from said source of mono-chromatic and coherent light andthe light rays from said source of white, multi-chromatic andnon-coherent light in a light beam.
 7. Device according to claim 1,wherein said holographic interference figure is obtained by an infraredsensor.
 8. Device according to claim 1, wherein said holographicinterference figure is obtained by a sensor whose wavelength is between300 nanometers and 2000 nanometers.
 9. Device according to claim 1,wherein said compressed image is obtained by an infrared sensor. 10.Device according to claim 1, wherein said compressed image is obtainedby a sensor whose wavelength is between 300 nanometers and 2000nanometers.
 11. Device according to claim 1, wherein said particularitysought in said sample is the presence of a kind and a species ofbacteria in a sample of saliva, of dental tartar sampling, nasalsecretions, blood or urine containing a set of bacteria of differentkinds and different species.
 12. Device according to claim 1, whereinsaid particularity sought in said sample is the presence of a moleculeor of a set of molecules exhibiting a particular transmittance in thelight spectrum concerned by the analysis.
 13. Device according to claim1, wherein said desired feature in said sample is the presence ofgametes in a sample of sperm.
 14. Apparatus according to claim 1,wherein the neural network is further designed to detect a microscopicimage of the sample from the hyperspectral holographic image.
 15. Methodfor holographic and hyperspectral measuring and analyzing of a sample,said method comprising: an illumination device illuminates said sample;and an acquisition device acquires an image containing the spectral andamplitude information of the light signal illuminating said sample; andholographic interference figures of a reference light beam and of alight beam having illuminated said sample containing the amplitude andphase information of the light signal illuminating said sample; and adevice for reconstructing the hyperspectral holographic image andanalyzing the amplitude, phase and spectrum properties of the lightilluminating said sample integrates a deep and convolutional neuralnetwork architectured to calculate a probability of presence of theparticularity sought in said sample from the hyperspectral holographicimage, and generate an image for each sought particularity whose valueof each pixel at the x and y coordinates corresponds to the probabilityof presence of said particularity at the same x and y coordinates ofsaid sample.
 16. A computer program comprising instructions which causea processor to perform the method of claim
 15. 17. A device according toclaim 2 wherein the illuminating beam is passing through the sample. 18.A device according to claim 2 wherein the illuminating beam is reflectedby the sample.
 19. A device for measuring a sample, said devicecomprising: a capture device for acquiring a compressed image of thesample containing spectral and amplitude information of the light signalilluminating said sample and holographic interference figures of areference light beam and of a light beam having illuminated said samplecontaining the amplitude and phase information of the light signalilluminating said sample; and an illumination device of said sample; anda device for reconstructing a microscopy image of the sample integratinga deep and convolutional neural network architectured to calculate alight intensity in said sample from the compressed image and theholographic interference figure of the beam of reference with the beamilluminating the sample, and generating an image whose value of eachpixel at the coordinates u and v corresponds to the light intensity atthe x and y coordinates of the plane of said sample; said deep andconvolutional neural network being architectured so as to merge theinformation of the sensors of the diffracted image and of the image ofthe holographic interference figure.